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   <div id="projectname"><a href="http://eigen.tuxfamily.org">Eigen-unsupported</a>
   &#160;<span id="projectnumber">3.4.90 (git rev 67eeba6e720c5745abc77ae6c92ce0a44aa7b7ae)</span>
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<div class="title">Class List</div>  </div>
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<div class="textblock">Here are the classes, structs, unions and interfaces with brief descriptions:</div><div class="directory">
<div class="levels">[detail level <span onclick="javascript:toggleLevel(1);">1</span><span onclick="javascript:toggleLevel(2);">2</span>]</div><table class="directory">
<tr id="row_0_" class="even"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_0_" class="arrow" onclick="toggleFolder('0_')">&#9660;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespaceEigen.html" target="_self">Eigen</a></td><td class="desc">Namespace containing all symbols from the Eigen library </td></tr>
<tr id="row_0_0_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1AlignedVector3.html" target="_self">AlignedVector3</a></td><td class="desc">A vectorization friendly 3D vector </td></tr>
<tr id="row_0_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1AutoDiffScalar.html" target="_self">AutoDiffScalar</a></td><td class="desc">A scalar type replacement with automatic differentiation capability </td></tr>
<tr id="row_0_2_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1BlockSparseMatrix.html" target="_self">BlockSparseMatrix</a></td><td class="desc">A versatile sparse matrix representation where each element is a block </td></tr>
<tr id="row_0_3_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1DGMRES.html" target="_self">DGMRES</a></td><td class="desc">A Restarted <a class="el" href="classEigen_1_1GMRES.html" title="A GMRES solver for sparse square problems.">GMRES</a> with deflation. This class implements a modification of the <a class="el" href="classEigen_1_1GMRES.html" title="A GMRES solver for sparse square problems.">GMRES</a> solver for sparse linear systems. The basis is built with modified Gram-Schmidt. At each restart, a few approximated eigenvectors corresponding to the smallest eigenvalues are used to build a preconditioner for the next cycle. This preconditioner for deflation can be combined with any other preconditioner, the <a class="elRef" href="../classEigen_1_1IncompleteLUT.html">IncompleteLUT</a> for instance. The preconditioner is applied at right of the matrix and the combination is multiplicative </td></tr>
<tr id="row_0_4_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1DynamicSGroup.html" target="_self">DynamicSGroup</a></td><td class="desc">Dynamic symmetry group </td></tr>
<tr id="row_0_5_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1EulerAngles.html" target="_self">EulerAngles</a></td><td class="desc">Represents a rotation in a 3 dimensional space as three Euler angles </td></tr>
<tr id="row_0_6_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1EulerSystem.html" target="_self">EulerSystem</a></td><td class="desc">Represents a fixed Euler rotation system </td></tr>
<tr id="row_0_7_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1GMRES.html" target="_self">GMRES</a></td><td class="desc">A <a class="el" href="classEigen_1_1GMRES.html" title="A GMRES solver for sparse square problems.">GMRES</a> solver for sparse square problems </td></tr>
<tr id="row_0_8_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1HybridNonLinearSolver.html" target="_self">HybridNonLinearSolver</a></td><td class="desc">Finds a zero of a system of n nonlinear functions in n variables by a modification of the Powell hybrid method ("dogleg") </td></tr>
<tr id="row_0_9_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1IDRS.html" target="_self">IDRS</a></td><td class="desc">The Induced Dimension Reduction method (IDR(s)) is a short-recurrences Krylov method for sparse square problems </td></tr>
<tr id="row_0_10_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1IDRSTABL.html" target="_self">IDRSTABL</a></td><td class="desc">The IDR(s)STAB(l) is a combination of IDR(s) and BiCGSTAB(l). It is a short-recurrences Krylov method for sparse square problems. It can outperform both IDR(s) and BiCGSTAB(l). IDR(s)STAB(l) generally closely follows the optimal <a class="el" href="classEigen_1_1GMRES.html" title="A GMRES solver for sparse square problems.">GMRES</a> convergence in terms of the number of Matrix-Vector products. However, without the increasing cost per iteration of <a class="el" href="classEigen_1_1GMRES.html" title="A GMRES solver for sparse square problems.">GMRES</a>. IDR(s)STAB(l) is suitable for both indefinite systems and systems with complex eigenvalues </td></tr>
<tr id="row_0_11_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1IterationController.html" target="_self">IterationController</a></td><td class="desc">Controls the iterations of the iterative solvers </td></tr>
<tr id="row_0_12_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1IterScaling.html" target="_self">IterScaling</a></td><td class="desc">Iterative scaling algorithm to equilibrate rows and column norms in matrices </td></tr>
<tr id="row_0_13_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1KdBVH.html" target="_self">KdBVH</a></td><td class="desc">A simple bounding volume hierarchy based on <a class="elRef" href="../classEigen_1_1AlignedBox.html">AlignedBox</a> </td></tr>
<tr id="row_0_14_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1KroneckerProduct.html" target="_self">KroneckerProduct</a></td><td class="desc">Kronecker tensor product helper class for dense matrices </td></tr>
<tr id="row_0_15_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1KroneckerProductBase.html" target="_self">KroneckerProductBase</a></td><td class="desc">The base class of dense and sparse Kronecker product </td></tr>
<tr id="row_0_16_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1KroneckerProductSparse.html" target="_self">KroneckerProductSparse</a></td><td class="desc">Kronecker tensor product helper class for sparse matrices </td></tr>
<tr id="row_0_17_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1LevenbergMarquardt.html" target="_self">LevenbergMarquardt</a></td><td class="desc">Performs non linear optimization over a non-linear function, using a variant of the Levenberg Marquardt algorithm </td></tr>
<tr id="row_0_18_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1MatrixComplexPowerReturnValue.html" target="_self">MatrixComplexPowerReturnValue</a></td><td class="desc">Proxy for the matrix power of some matrix (expression) </td></tr>
<tr id="row_0_19_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structEigen_1_1MatrixExponentialReturnValue.html" target="_self">MatrixExponentialReturnValue</a></td><td class="desc">Proxy for the matrix exponential of some matrix (expression) </td></tr>
<tr id="row_0_20_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1MatrixFunctionReturnValue.html" target="_self">MatrixFunctionReturnValue</a></td><td class="desc">Proxy for the matrix function of some matrix (expression) </td></tr>
<tr id="row_0_21_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1MatrixLogarithmReturnValue.html" target="_self">MatrixLogarithmReturnValue</a></td><td class="desc">Proxy for the matrix logarithm of some matrix (expression) </td></tr>
<tr id="row_0_22_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1MatrixMarketIterator.html" target="_self">MatrixMarketIterator</a></td><td class="desc">Iterator to browse matrices from a specified folder </td></tr>
<tr id="row_0_23_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1MatrixPower.html" target="_self">MatrixPower</a></td><td class="desc">Class for computing matrix powers </td></tr>
<tr id="row_0_24_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1MatrixPowerAtomic.html" target="_self">MatrixPowerAtomic</a></td><td class="desc">Class for computing matrix powers </td></tr>
<tr id="row_0_25_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1MatrixPowerParenthesesReturnValue.html" target="_self">MatrixPowerParenthesesReturnValue</a></td><td class="desc">Proxy for the matrix power of some matrix </td></tr>
<tr id="row_0_26_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1MatrixPowerReturnValue.html" target="_self">MatrixPowerReturnValue</a></td><td class="desc">Proxy for the matrix power of some matrix (expression) </td></tr>
<tr id="row_0_27_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1MatrixSquareRootReturnValue.html" target="_self">MatrixSquareRootReturnValue</a></td><td class="desc">Proxy for the matrix square root of some matrix (expression) </td></tr>
<tr id="row_0_28_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1MaxSizeVector.html" target="_self">MaxSizeVector</a></td><td class="desc">The <a class="el" href="classEigen_1_1MaxSizeVector.html" title="The MaxSizeVector class.">MaxSizeVector</a> class </td></tr>
<tr id="row_0_29_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1MINRES.html" target="_self">MINRES</a></td><td class="desc">A minimal residual solver for sparse symmetric problems </td></tr>
<tr id="row_0_30_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1NNLS.html" target="_self">NNLS</a></td><td class="desc">Implementation of the Non-Negative Least Squares (<a class="el" href="classEigen_1_1NNLS.html" title="Implementation of the Non-Negative Least Squares (NNLS) algorithm.">NNLS</a>) algorithm </td></tr>
<tr id="row_0_31_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1NumericalDiff.html" target="_self">NumericalDiff</a></td><td class="desc"></td></tr>
<tr id="row_0_32_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structEigen_1_1NumTraits_3_01mpfr_1_1mpreal_01_4.html" target="_self">NumTraits&lt; mpfr::mpreal &gt;</a></td><td class="desc"></td></tr>
<tr id="row_0_33_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1PolynomialSolver.html" target="_self">PolynomialSolver</a></td><td class="desc">A polynomial solver </td></tr>
<tr id="row_0_34_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1PolynomialSolverBase.html" target="_self">PolynomialSolverBase</a></td><td class="desc">Defined to be inherited by polynomial solvers: it provides convenient methods such as </td></tr>
<tr id="row_0_35_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1RandomSetter.html" target="_self">RandomSetter</a></td><td class="desc">The <a class="el" href="classEigen_1_1RandomSetter.html" title="The RandomSetter is a wrapper object allowing to set/update a sparse matrix with random access.">RandomSetter</a> is a wrapper object allowing to set/update a sparse matrix with random access </td></tr>
<tr id="row_0_36_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1SGroup.html" target="_self">SGroup</a></td><td class="desc">Symmetry group, initialized from template arguments </td></tr>
<tr id="row_0_37_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1SkylineInplaceLU.html" target="_self">SkylineInplaceLU</a></td><td class="desc">Inplace LU decomposition of a skyline matrix and associated features </td></tr>
<tr id="row_0_38_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1SkylineMatrix.html" target="_self">SkylineMatrix</a></td><td class="desc">The main skyline matrix class </td></tr>
<tr id="row_0_39_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1SkylineMatrixBase.html" target="_self">SkylineMatrixBase</a></td><td class="desc">Base class of any skyline matrices or skyline expressions </td></tr>
<tr id="row_0_40_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1SkylineStorage.html" target="_self">SkylineStorage</a></td><td class="desc"></td></tr>
<tr id="row_0_41_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1Spline.html" target="_self">Spline</a></td><td class="desc">A class representing multi-dimensional spline curves </td></tr>
<tr id="row_0_42_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structEigen_1_1SplineFitting.html" target="_self">SplineFitting</a></td><td class="desc"><a class="el" href="classEigen_1_1Spline.html" title="A class representing multi-dimensional spline curves.">Spline</a> fitting methods </td></tr>
<tr id="row_0_43_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structEigen_1_1SplineTraits_3_01Spline_3_01Scalar___00_01Dim___00_01Degree___01_4_00_01__DerivativeOrder_01_4.html" target="_self">SplineTraits&lt; Spline&lt; Scalar_, Dim_, Degree_ &gt;, _DerivativeOrder &gt;</a></td><td class="desc">Compile-time attributes of the <a class="el" href="classEigen_1_1Spline.html" title="A class representing multi-dimensional spline curves.">Spline</a> class for fixed degree </td></tr>
<tr id="row_0_44_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structEigen_1_1SplineTraits_3_01Spline_3_01Scalar___00_01Dim___00_01Degree___01_4_00_01Dynamic_01_4.html" target="_self">SplineTraits&lt; Spline&lt; Scalar_, Dim_, Degree_ &gt;, Dynamic &gt;</a></td><td class="desc">Compile-time attributes of the <a class="el" href="classEigen_1_1Spline.html" title="A class representing multi-dimensional spline curves.">Spline</a> class for Dynamic degree </td></tr>
<tr id="row_0_45_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1StaticSGroup.html" target="_self">StaticSGroup</a></td><td class="desc">Static symmetry group </td></tr>
<tr id="row_0_46_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structEigen_1_1StdMapTraits.html" target="_self">StdMapTraits</a></td><td class="desc"></td></tr>
<tr id="row_0_47_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structEigen_1_1StdUnorderedMapTraits.html" target="_self">StdUnorderedMapTraits</a></td><td class="desc"></td></tr>
<tr id="row_0_48_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1Tensor.html" target="_self">Tensor</a></td><td class="desc">The tensor class </td></tr>
<tr id="row_0_49_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1TensorAsyncDevice.html" target="_self">TensorAsyncDevice</a></td><td class="desc">Pseudo expression providing an operator = that will evaluate its argument asynchronously on the specified device. Currently only ThreadPoolDevice implements proper asynchronous execution, while the default and GPU devices just run the expression synchronously and call m_done() on completion. </td></tr>
<tr id="row_0_50_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1TensorBase.html" target="_self">TensorBase</a></td><td class="desc">The tensor base class </td></tr>
<tr id="row_0_51_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1TensorConcatenationOp.html" target="_self">TensorConcatenationOp</a></td><td class="desc"><a class="el" href="classEigen_1_1Tensor.html" title="The tensor class.">Tensor</a> concatenation class </td></tr>
<tr id="row_0_52_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1TensorConversionOp.html" target="_self">TensorConversionOp</a></td><td class="desc"><a class="el" href="classEigen_1_1Tensor.html" title="The tensor class.">Tensor</a> conversion class. This class makes it possible to vectorize type casting operations when the number of scalars per packet in the source and the destination type differ </td></tr>
<tr id="row_0_53_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1TensorCustomBinaryOp.html" target="_self">TensorCustomBinaryOp</a></td><td class="desc"><a class="el" href="classEigen_1_1Tensor.html" title="The tensor class.">Tensor</a> custom class </td></tr>
<tr id="row_0_54_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1TensorCustomUnaryOp.html" target="_self">TensorCustomUnaryOp</a></td><td class="desc"><a class="el" href="classEigen_1_1Tensor.html" title="The tensor class.">Tensor</a> custom class </td></tr>
<tr id="row_0_55_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1TensorDevice.html" target="_self">TensorDevice</a></td><td class="desc">Pseudo expression providing an operator = that will evaluate its argument on the specified computing 'device' (GPU, thread pool, ...) </td></tr>
<tr id="row_0_56_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structEigen_1_1TensorEvaluator.html" target="_self">TensorEvaluator</a></td><td class="desc">A cost model used to limit the number of threads used for evaluating tensor expression </td></tr>
<tr id="row_0_57_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1TensorFixedSize.html" target="_self">TensorFixedSize</a></td><td class="desc">The fixed sized version of the tensor class </td></tr>
<tr id="row_0_58_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1TensorGeneratorOp.html" target="_self">TensorGeneratorOp</a></td><td class="desc"><a class="el" href="classEigen_1_1Tensor.html" title="The tensor class.">Tensor</a> generator class </td></tr>
<tr id="row_0_59_" class="even"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1TensorMap.html" target="_self">TensorMap</a></td><td class="desc">A tensor expression mapping an existing array of data </td></tr>
<tr id="row_0_60_"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classEigen_1_1TensorRef.html" target="_self">TensorRef</a></td><td class="desc">A reference to a tensor expression The expression will be evaluated lazily (as much as possible) </td></tr>
<tr id="row_1_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorAssign.html" target="_self">TensorAssign</a></td><td class="desc">The tensor assignment class </td></tr>
<tr id="row_2_"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorBroadcasting.html" target="_self">TensorBroadcasting</a></td><td class="desc">Tensor broadcasting class </td></tr>
<tr id="row_3_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorContraction.html" target="_self">TensorContraction</a></td><td class="desc">Tensor contraction class </td></tr>
<tr id="row_4_"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorConvolution.html" target="_self">TensorConvolution</a></td><td class="desc">Tensor convolution class </td></tr>
<tr id="row_5_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorExecutor.html" target="_self">TensorExecutor</a></td><td class="desc">The tensor executor class </td></tr>
<tr id="row_6_"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorExpr.html" target="_self">TensorExpr</a></td><td class="desc">Tensor expression classes </td></tr>
<tr id="row_7_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorFFT.html" target="_self">TensorFFT</a></td><td class="desc">Tensor FFT class </td></tr>
<tr id="row_8_"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorForcedEval.html" target="_self">TensorForcedEval</a></td><td class="desc">Tensor reshaping class </td></tr>
<tr id="row_9_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorImagePatch.html" target="_self">TensorImagePatch</a></td><td class="desc">Patch extraction specialized for image processing. This assumes that the input has a least 3 dimensions ordered as follow: 1st dimension: channels (of size d) 2nd dimension: rows (of size r) 3rd dimension: columns (of size c) There can be additional dimensions such as time (for video) or batch (for bulk processing after the first 3. Calling the image patch code with patch_rows and patch_cols is equivalent to calling the regular patch extraction code with parameters d, patch_rows, patch_cols, and 1 for all the additional dimensions </td></tr>
<tr id="row_10_"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorIndexPair.html" target="_self">TensorIndexPair</a></td><td class="desc">Tensor + Index Pair class </td></tr>
<tr id="row_11_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorInflation.html" target="_self">TensorInflation</a></td><td class="desc">Tensor inflation class </td></tr>
<tr id="row_12_"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorInitializer.html" target="_self">TensorInitializer</a></td><td class="desc">Helper template to initialize Tensors from std::initializer_lists </td></tr>
<tr id="row_13_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorKChippingReshaping.html" target="_self">TensorKChippingReshaping</a></td><td class="desc">A chip is a thin slice, corresponding to a column or a row in a 2-d tensor </td></tr>
<tr id="row_14_"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorLayoutSwap.html" target="_self">TensorLayoutSwap</a></td><td class="desc">Swap the layout from col-major to row-major, or row-major to col-major, and invert the order of the dimensions </td></tr>
<tr id="row_15_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorPadding.html" target="_self">TensorPadding</a></td><td class="desc">Tensor padding class. At the moment only padding with a constant value is supported </td></tr>
<tr id="row_16_"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorPairIndex.html" target="_self">TensorPairIndex</a></td><td class="desc">Converts to Tensor&lt;Pair&lt;Index, Scalar&gt; &gt; and reduces to Tensor&lt;Index&gt; </td></tr>
<tr id="row_17_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorPatch.html" target="_self">TensorPatch</a></td><td class="desc">Tensor patch class </td></tr>
<tr id="row_18_"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorReduction.html" target="_self">TensorReduction</a></td><td class="desc">Tensor reduction class </td></tr>
<tr id="row_19_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorReshaping.html" target="_self">TensorReshaping</a></td><td class="desc">Tensor reshaping class </td></tr>
<tr id="row_20_"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorReverse.html" target="_self">TensorReverse</a></td><td class="desc">Tensor reverse elements class </td></tr>
<tr id="row_21_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorScan.html" target="_self">TensorScan</a></td><td class="desc">Tensor scan class </td></tr>
<tr id="row_22_"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorShuffling.html" target="_self">TensorShuffling</a></td><td class="desc">Tensor shuffling class </td></tr>
<tr id="row_23_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorSlicing.html" target="_self">TensorSlicing</a></td><td class="desc">Tensor slicing class </td></tr>
<tr id="row_24_"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorStriding.html" target="_self">TensorStriding</a></td><td class="desc">Tensor striding class </td></tr>
<tr id="row_25_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorTrace.html" target="_self">TensorTrace</a></td><td class="desc">Tensor Trace class </td></tr>
<tr id="row_26_"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classTensorVolumePatch.html" target="_self">TensorVolumePatch</a></td><td class="desc">Patch extraction specialized for processing of volumetric data. This assumes that the input has a least 4 dimensions ordered as follows: </td></tr>
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